Detection of the broken rotor bars of squirrel-cage induction motors based on normalized least mean square filter and Hilbert envelope analysis

2016 ◽  
Vol 98 (3) ◽  
pp. 245-256 ◽  
Author(s):  
A. Unsal ◽  
A. Kabul
Keyword(s):  
Induction Motors ◽  
Mean Square ◽  
Least Mean Square ◽  
Envelope Analysis ◽  
Broken Rotor Bars ◽  
Squirrel Cage ◽  
Normalized Least Mean Square ◽  
Hilbert Envelope

Detection of broken rotor bars of induction motors based on the combination of Hilbert envelope analysis and Shannon entropy

2021 ◽  
Vol 88 (1) ◽  
pp. 45-58
Author(s):  
Ahmet Kabul ◽  
Abdurrahman Ünsal
Keyword(s):  
Fundamental Frequency ◽  
Shannon Entropy ◽  
Evaluation System ◽  
Induction Motors ◽  
Loading Conditions ◽  
Envelope Analysis ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
Lower Amplitude ◽  
Hilbert Envelope

AbstractBroken rotor bar (BRB) is one of the most common fault types of induction motors. One of the common methods to detect the broken rotor bars is the observation of the characteristic sideband frequencies in the stator current. If the motor is lightly loaded, the sideband harmonics are attached to the fundamental frequency of the main supply and the amplitudes of these harmonics are quite low. Therefore, it is difficult to detect the broken rotor bars under light loading conditions by using conventional motor current signature analysis (MCSA) methods. Moreover, in some cases, the sideband harmonics of fundamental frequency may exist although there is no rotor fault in induction motors due to load oscillations. Therefore, there is a risk for false broken rotor bars alarm with the existence of lower amplitude of harmonics. This paper provides an alternative approach for the detection of broken rotor bars by applying Hilbert envelope analysis along with Shannon entropy to stator current signals. The proposed method includes two-stage evaluation system to eliminate false BRB alarms such as detecting sidebands from envelope spectrum and calculating entropy rates from envelope signals. The results are verified experimentally under 25 %, 50 %, 75 % and 100 % loading conditions.

Induction motors broken rotor bars detection using RPVM and neural network

2019 ◽  
Vol 38 (2) ◽  
pp. 596-615 ◽  
Author(s):  
Saddam Bensaoucha ◽  
Sid Ahmed Bessedik ◽  
Aissa Ameur ◽  
Ali Teta
Keyword(s):  
Design Methodology ◽  
Induction Motors ◽  
Content Type ◽  
Broken Rotor Bars ◽  
Squirrel Cage ◽  
Detection And Identification ◽  
Squirrel Cage Induction Motor ◽  
Simulation Results ◽  
Cage Induction Motor ◽  
Diagnosis Technique

Purpose The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates. Design/methodology/approach First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs. Findings The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach. Originality/value The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).

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